Generating a demonstration dataset with the desired properties can be difficult, expensive, or time consuming. Unlike lower control frequency real world tasks [1]}, obtaining quality teleoperation data for high acceleration tasks such as precision table tennis is itself a difficult engineering challenge. Previous approaches to generating demonstration data in the table tennis setting [2]} involve customized hybrid sim-to-real training, including a “rebound model", whose parameters needed to be tuned empirically to enable accurate sim2real transfer.
